Notes from GraphXD sprints

Also see the example folder for concrete code

From discussions with Nelle Varoquaux, Aric Hagberg, and Dan Schult

• restrict to ‘small’ networks (few hundred to thousand)
• there are many choices in plotting mapping graph attributes -> visual properties
  • (x, y) layout
  • colors
  • labels
  • edges style
  • interactivity (hover / pick)
  • edge routing
• want to be able to update the plot in response to updating the network
• want to make it easily extensible
• performance?

From discussion with large group of network practitioners

• need “seaborn for networks” - heursitics for edge style
• also want “seaborn for network statistics”
• look at igraph - have lots of layout engines
• people tend to use different plots for data exploration vs publication
• functions for different on graph size
• input to layout engine should be flexible
• provide way to go to the bottom!

ball and edge

• per-vertex/edges labels
• per-vertex/edges annotation box (maybe?)
• per-vertex/edge artists / subplots - excited about pie charts - do this as roll-over / tool-tip
• mark sub-graphs
• per-node style
• per-edge style - directed edges - un-directed edges - multi-edges - arbitrary path

adjacency matrix view

• show adjacency matrix
• show anything at all in the gutters (all 8 places)

trees

• dendograms

sankey / flow

• this may be better interface to the existing sankey functionality in Matplotlib

initial target cases

• re-make jarod’s slides - small (25 nodes)
• a 1000 node random graph - no labels, transparency, color per edge
• A tree of some sort
• splicing graph
• neural networks
• some flow network